NLP之LSTM与BiLSTM

代码展示

import pandas as pd
import tensorflow as tf
tf.random.set_seed(1)
df = pd.read_csv("../data/Clothing Reviews.csv")
print(df.info())df['Review Text'] = df['Review Text'].astype(str)
x_train = df['Review Text']
y_train = df['Rating']
print(y_train.unique())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23486 entries, 0 to 23485
Data columns (total 11 columns):#   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 0   Unnamed: 0               23486 non-null  int64 1   Clothing ID              23486 non-null  int64 2   Age                      23486 non-null  int64 3   Title                    19676 non-null  object4   Review Text              22641 non-null  object5   Rating                   23486 non-null  int64 6   Recommended IND          23486 non-null  int64 7   Positive Feedback Count  23486 non-null  int64 8   Division Name            23472 non-null  object9   Department Name          23472 non-null  object10  Class Name               23472 non-null  object

[4 5 3 2 1]

from tensorflow.keras.preprocessing.text import Tokenizerdict_size = 14848
tokenizer = Tokenizer(num_words=dict_size)tokenizer.fit_on_texts(x_train)
print(len(tokenizer.word_index),tokenizer.index_word)x_train_tokenized = tokenizer.texts_to_sequences(x_train)
from tensorflow.keras.preprocessing.sequence import pad_sequences
max_comment_length = 120
x_train = pad_sequences(x_train_tokenized,maxlen=max_comment_length)for v in x_train[:10]:print(v,len(v))

# 构建RNN神经网络
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,SimpleRNN,Embedding,LSTM,Bidirectional
import tensorflow as tfrnn = Sequential()
# 对于rnn来说首先进行词向量的操作
rnn.add(Embedding(input_dim=dict_size,output_dim=60,input_length=max_comment_length))
# RNN：simple_rnn (SimpleRNN)  (None, 100)   16100
# LSTM：simple_rnn (SimpleRNN)  (None, 100)  64400
rnn.add(Bidirectional(LSTM(units=100)))  # 第二层构建了100个RNN神经元
rnn.add(Dense(units=10,activation=tf.nn.relu))
rnn.add(Dense(units=6,activation=tf.nn.softmax))  # 输出分类的结果
rnn.compile(loss='sparse_categorical_crossentropy',optimizer="adam",metrics=['accuracy'])
print(rnn.summary())
result = rnn.fit(x_train,y_train,batch_size=64,validation_split=0.3,epochs=10)
print(result)
print(result.history)

代码解读

首先，我们来总结这段代码的流程：

1. 导入了必要的TensorFlow Keras模块。
2. 初始化了一个Sequential模型，这表示我们的模型会按顺序堆叠各层。
3. 添加了一个Embedding层，用于将整数索引（对应词汇）转换为密集向量。
4. 添加了一个双向LSTM层，其中包含100个神经元。
5. 添加了两个Dense全连接层，分别包含10个和6个神经元。
6. 使用sparse_categorical_crossentropy损失函数编译了模型。
7. 打印了模型的摘要。
8. 使用给定的训练数据和验证数据对模型进行了训练。
9. 打印了训练的结果。

现在，让我们逐行解读代码：

1. 导入依赖:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,SimpleRNN,Embedding,LSTM,Bidirectional
import tensorflow as tf

你导入了创建和训练RNN模型所需的TensorFlow Keras库。

2. 初始化模型:

rnn = Sequential()

你选择了一个顺序模型，这意味着你可以简单地按顺序添加层。

3. 添加Embedding层:

rnn.add(Embedding(input_dim=dict_size,output_dim=60,input_length=max_comment_length))

此层将整数索引转换为固定大小的向量。dict_size是词汇表的大小，max_comment_length是输入评论的最大长度。

4. 添加LSTM层:

rnn.add(Bidirectional(LSTM(units=100)))

你选择了双向LSTM，这意味着它会考虑过去和未来的信息。它有100个神经元。

5. 添加全连接层:

rnn.add(Dense(units=10,activation=tf.nn.relu))
rnn.add(Dense(units=6,activation=tf.nn.softmax))

这两个Dense层用于模型的输出，最后一层使用softmax激活函数进行6类的分类。

6. 编译模型:

rnn.compile(loss='sparse_categorical_crossentropy',optimizer="adam",metrics=['accuracy'])

你选择了一个适合分类问题的损失函数，并选择了adam优化器。

7. 显示模型摘要:

print(rnn.summary())

这将展示模型的结构和参数数量。

Model: "sequential"
_________________________________________________________________Layer (type)                Output Shape              Param #   
=================================================================embedding (Embedding)       (None, 120, 60)           890880    bidirectional (Bidirectiona  (None, 200)              128800    l)                                                              dense (Dense)               (None, 10)                2010      dense_1 (Dense)             (None, 6)                 66        =================================================================
Total params: 1,021,756
Trainable params: 1,021,756
Non-trainable params: 0
_________________________________________________________________
None

8. 训练模型:

result = rnn.fit(x_train,y_train,batch_size=64,validation_split=0.3,epochs=10)

你用训练数据集训练了模型，其中30%的数据用作验证，训练了10个周期。

Epoch 1/10
257/257 [==============================] - 74s 258ms/step - loss: 1.2142 - accuracy: 0.5470 - val_loss: 1.0998 - val_accuracy: 0.5521
Epoch 2/10
257/257 [==============================] - 57s 221ms/step - loss: 0.9335 - accuracy: 0.6293 - val_loss: 0.9554 - val_accuracy: 0.6094
Epoch 3/10
257/257 [==============================] - 59s 229ms/step - loss: 0.8363 - accuracy: 0.6616 - val_loss: 0.9321 - val_accuracy: 0.6168
Epoch 4/10
257/257 [==============================] - 61s 236ms/step - loss: 0.7795 - accuracy: 0.6833 - val_loss: 0.9812 - val_accuracy: 0.6089
Epoch 5/10
257/257 [==============================] - 56s 217ms/step - loss: 0.7281 - accuracy: 0.7010 - val_loss: 0.9559 - val_accuracy: 0.6043
Epoch 6/10
257/257 [==============================] - 56s 219ms/step - loss: 0.6934 - accuracy: 0.7156 - val_loss: 1.0197 - val_accuracy: 0.5999
Epoch 7/10
257/257 [==============================] - 57s 220ms/step - loss: 0.6514 - accuracy: 0.7364 - val_loss: 1.1192 - val_accuracy: 0.6080
Epoch 8/10
257/257 [==============================] - 57s 222ms/step - loss: 0.6258 - accuracy: 0.7486 - val_loss: 1.1350 - val_accuracy: 0.6100
Epoch 9/10
257/257 [==============================] - 57s 220ms/step - loss: 0.5839 - accuracy: 0.7749 - val_loss: 1.1537 - val_accuracy: 0.6019
Epoch 10/10
257/257 [==============================] - 57s 222ms/step - loss: 0.5424 - accuracy: 0.7945 - val_loss: 1.1715 - val_accuracy: 0.5744
<keras.callbacks.History object at 0x00000244DCE06D90>

9. 显示训练结果:

print(result)

<keras.callbacks.History object at 0x0000013AEAAE1A30>

print(result.history)

{'loss': [1.2142471075057983, 0.9334620833396912, 0.8363043069839478, 0.7795010805130005, 0.7280740141868591, 0.693393349647522, 0.6514003872871399, 0.6257606744766235, 0.5839114189147949, 0.5423741340637207], 
'accuracy': [0.5469586253166199, 0.6292579174041748, 0.6616179943084717, 0.6833333373069763, 0.7010340690612793, 0.7156326174736023, 0.7363746762275696, 0.748600959777832, 0.7748783230781555, 0.7944647073745728], 
'val_loss': [1.0997602939605713, 0.9553984999656677, 0.932131290435791, 0.9812102317810059, 0.9558586478233337, 1.019730806350708, 1.11918044090271, 1.1349923610687256, 1.1536787748336792, 1.1715185642242432], 
'val_accuracy': [0.5520862936973572, 0.609423816204071, 0.6168038845062256, 0.6088560819625854, 0.6043145060539246, 0.5999148488044739, 0.6080045700073242, 0.6099914908409119, 0.6019017696380615, 0.574368417263031]
}

这将展示训练过程中的损失和准确性等信息。

双向LSTM介绍（BiLSTM）

例子：